Current findings of neighboring genes involved in plant specialized metabolism provide the genomic signatures of metabolic evolution. Two such genomic features, namely, (i) metabolic gene cluster and (ii) neo-functionalization of tandem gene duplications, represent key factors corresponding to the creation of metabolic diversity of plant specialized metabolism. So far, several terpenoid and alkaloid biosynthetic genes have been characterized with gene clusters in some plants. On the other hand, some modification genes involved in flavonoid and glucosinolate biosynthesis were found to arise via gene neo-functionalization. Although the occurrence of both types of metabolic evolution are different, the neighboring genes are generally regulated by the same or related regulation factors. Therefore, the translation-based approaches associated with genomics, and transcriptomics are able to be employed for functional genomics focusing on plant secondary metabolism. Here, we present a survey of the current understanding of neighboring genes involved in plant secondary metabolism. Additionally, a genomic overview of neighboring genes of four model plants and transcriptional co-expression network neighboring genes to detect metabolic gene clusters in Arabidopsis is provided. Finally, the insights functional genomics have provided concerning the evolution and mechanistic regulation of both the formation and operation of metabolic neighboring clusters is discussed.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285293 | PMC |
http://dx.doi.org/10.3390/plants9050622 | DOI Listing |
G3 (Bethesda)
January 2025
Department of Integrative Biology, University of California at Berkeley, Berkeley, CA 94720, USA.
Atahualpa is a rural village located in coastal Ecuador, a region that has been inhabited by people as early as 10,000 years ago. The traditional diet of their indigenous inhabitants is rich in oily fish and they have, therefore, served as a model for investigating the beneficial effects of such a diet. However, the genetic background of this population has not been studied.
View Article and Find Full Text PDFMol Biol Rep
January 2025
School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, People's Republic of China.
Background: Paeonia lactiflora Pall., a member of Paeoniaceae family, is a medicinal herb widely used in traditional Chinese medicine. Chloroplasts are multifunctional organelles containing distinct genetic material.
View Article and Find Full Text PDFBiol Aujourdhui
January 2025
Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000 Versailles, France.
Today, weed control in agricultural systems is largely based on the use of synthetic pesticides. However, the use of these compounds is increasingly controversial among farmers and consumers, who point to their harmful properties for human health and the environment. In this context, the development of eco-friendly agricultural approaches and practices is becoming essential, and allelopathy represents a promising solution.
View Article and Find Full Text PDFDiagn Microbiol Infect Dis
January 2025
National Reference Laboratory of Control and Monitoring of Antibiotic Resistance (NRL-CMAR), Department Microbiology, National Center of Infectious and Parasitic Diseases (NCIPD), 26 Yanko Sakazov Blvd., Sofia, Bulgaria.
Increased incidence of Clostridioides difficile infections were documented in Bulgarian hospitals during COVID-19. WGS was performed on 39 isolates from seven hospitals during 2015-2022. Antimicrobial resistance and toxin genes were inferred from genomes.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
School of Environmental Science and Engineering, Hainan University, Haikou 570228, China.
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!